Methods and systems for evaluating microsatellite instability status

ABSTRACT

Methods for evaluating microsatellite instability (MSI) analyze nucleic acid sequence reads corresponding to a plurality of marker regions for MSI. The marker regions may include long homopolymers and/or short tandem repeats (STRs). For a target homopolymer, a histogram of homopolymer signal values is calculated based on flow space signal measurements for the homopolymer region in the sequence reads. A score per marker based on features of the histogram of homopolymer signal values is determined for each marker region corresponding to the target homopolymers. For a target STR, the method includes calculating a histogram of repeat lengths for sequence reads corresponding to the marker region of the target STR. A score per STR marker is calculated based on features of the histogram of repeat lengths. A plurality of per marker scores may be combined to form a total MSI score for the sample.

CROSS-REFERENCE

This application is a continuation of U.S. application Ser. No.16/595,681 filed Oct. 8, 2019, which claims the benefit under 35 U.S.C.§ 119(e) of U.S. Application No. 62/858,387 filed Jun. 7, 2019, U.S.Application No. 62/785,596 filed Dec. 27, 2018, and U.S. Application No.62/745,161 filed Oct. 12, 2018. The entire contents of theaforementioned applications are incorporated by reference herein.

FIELD

This application generally relates to methods, systems,computer-readable media, compositions, and kits for detection ofmicrosatellite instability (MSI), and, more specifically, to methods,systems, computer-readable media, compositions, and kits for detectionof MSI based on or using nucleic acid sequencing data andnext-generation sequencing technology or systems in conjunction withprimers for detection of one or more MSI events of interest.

SUMMARY

Cancer-associated instabilities at microsatellite locations throughoutthe genome have been shown to be predictive of response to immunotherapytreatment. A Microsatellite Instability High (MSI-H) status can resultwhen the DNA Mismatch Repair (MMR) system fails to work probably and isassociated with hypermutability of short DNA sequence repeats,microsatellite locations, throughout the genome. In 1997, NCIrecommended utilizing a panel of five MSI markers for detectingcolorectal cancer (CRC). The traditional approach uses capillaryelectrophoresis (CE) and utilizes the difference in marker profile amonga tumor/normal tissue pair to determine the MSI Status of that tumor.

Recently, there has been a growing demand to develop more sensitivesolutions to MSI detection with a larger number of markers. NextGeneration Sequencing (NGS) provides a natural solution for that demandwith the ability to process multiple samples and a large number ofmarkers. MSI markers can be very long homopolymers, dinucleotide(di-nuc) and trinucleotide (tri-nuc) short tandem repeats (STRs). Thesetypes of motifs are not easily amplified or sequenced accurately due tothe existence of different artifacts including stutter.

There is a need for new and improved methods, systems, computer-readablemedia, compositions, and kits for better and more accurate detection ofMSI, including better and more accurate detection of genomic regions forMSI status evaluation. There is a need for accurately evaluating MSIstatus based on different types of MSI markers, such as for longhomopolymers and STRs. There is a need for determining MSI status usingtumor only samples.

According to an exemplary embodiment, there is provided a method fordetecting microsatellite instability (MSI) in a sample, including: (1)receiving a plurality of nucleic acid sequence reads corresponding to aplurality of marker regions for MSI, wherein each of the sequence readsincludes a left flank sequence, right flank sequence and a repeat regionof bases positioned between a rightmost base of the left flank sequenceand a leftmost base of the right flank sequence, wherein the repeatregion includes a number of repeats of a repeated sequence of basescorresponding to a particular marker region of the plurality of markerregions; (2) for each of the sequence reads, aligning at least a portionthe left flank sequence with a reference left flank, wherein thereference left flank borders a reference repeat region of a referencenucleic acid sequence corresponding to the particular marker region; (3)for the repeat region corresponding to a target homopolymer in thesequence reads, calculating a histogram of homopolymer signal valuesbased on flow space signal measurements for the target homopolymer,wherein at least a portion of the marker regions corresponds to targethomopolymers; (4) determining a score per marker based on features ofthe histogram of homopolymer signal values for each marker regioncorresponding to the target homopolymers to produce a plurality ofscores; and (5) combining the plurality of scores to form a total MSIscore for the sample.

According to an exemplary embodiment, there is providedcomputer-readable media comprising machine-readable instructions that,when loaded in a machine-readable memory and executed by the processor,are configured to cause a system to perform a method detectingmicrosatellite instability (MSI) in a sample, the method including: (1)receiving a plurality of nucleic acid sequence reads corresponding to aplurality of marker regions for MSI, wherein each of the sequence readsincludes a left flank sequence, right flank sequence and a repeat regionof bases positioned between a rightmost base of the left flank sequenceand a leftmost base of the right flank sequence, wherein the repeatregion includes a number of repeats of a repeated sequence of basescorresponding to a particular marker region of the plurality of markerregions; (2) for each of the sequence reads, aligning at least a portionthe left flank sequence with a reference left flank, wherein thereference left flank borders a reference repeat region of a referencenucleic acid sequence corresponding to the particular marker region; (3)for the repeat region corresponding to a target homopolymer, calculatinga histogram of homopolymer signal values based on flow space signalmeasurements for the target homopolymer, wherein at least a portion ofthe marker regions corresponds to target homopolymers; (4) determining ascore per marker based on features of the histogram of homopolymersignal values for each marker region corresponding to the targethomopolymers to produce a plurality of scores; and (5) combining theplurality of scores to form a total MSI score for the sample.

According to an exemplary embodiment, there is provided a system fordetecting microsatellite instability (MSI), including a machine-readablememory and a processor configured to execute machine-readableinstructions, which, when executed by the processor, cause the system toperform a method for detecting MSI in a sample, the method including: 1)receiving a plurality of nucleic acid sequence reads corresponding to aplurality of marker regions for MSI, wherein each of the sequence readsincludes a left flank sequence, right flank sequence and a repeat regionof bases positioned between a rightmost base of the left flank sequenceand a leftmost base of the right flank sequence, wherein the repeatregion includes a number of repeats of a repeated sequence of basescorresponding to a particular marker region of the plurality of markerregions; (2) for each of the sequence reads, aligning at least a portionthe left flank sequence with a reference left flank, wherein thereference left flank borders a reference repeat region of a referencenucleic acid sequence corresponding to the particular marker region; (3)for the repeat region corresponding to a target homopolymer in thesequence reads, calculating a histogram of homopolymer signal valuesbased on flow space signal measurements for the target homopolymer,wherein at least a portion of the marker regions corresponds to targethomopolymers; (4) determining a score per marker based on features ofthe histogram of homopolymer signal values for each marker regioncorresponding to the target homopolymers to produce a plurality ofscores; and (5) combining the plurality of scores to form a total MSIscore for the sample.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the embodiments will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,and the accompanying drawings of which:

FIG. 1 illustrates examples of nucleic acid sequences having MSI markerregions and flank regions.

FIG. 2 illustrates an example of a sequence read having an STR region.

FIG. 3 is a block diagram of a method of determining an MSI score,according to an exemplary embodiment.

FIG. 4 shows an example of superimposed flow space signal measurementsfor two homopolymer sequence reads that are MSI-H and MSI-L,respectively.

FIGS. 5A-5D show examples of histograms of HP signal values for tumorand normal samples.

FIGS. 6A and 6B shows examples of histograms of HP signal values fortumor and normal samples.

FIG. 7 is a plot of an example of a sigmoid function.

FIGS. 8A-8D show examples of histograms of repeat lengths for MSI-H andmatched normal samples.

FIG. 9 gives an exemplary table of results of per marker scores andtotal MSI scores for several markers in six samples with known MSIstatus.

FIG. 10 gives an exemplary table of results of testing of MSI statususing capillary electrophoresis (CE).

FIG. 11 gives an exemplary table of results of testing of MSI statususing the total MSI score determined by the NGS methods describedherein.

FIG. 12 shows an exemplary representation of flow space signalmeasurements from which base calls may be made.

FIG. 13 is a schematic diagram of an exemplary system for a nucleic acidsequencer, in accordance with various embodiments.

FIG. 14 is a block diagram of a method for hybrid control, according toan exemplary embodiment.

FIG. 15 gives a plot of exemplary results for MSI scores determinedusing on-chip control samples and hybrid control.

FIGS. 16A and 16B illustrate examples of distributions of homopolymersignal values for on-chip control, MSS/Normal and MSI-High.

FIGS. 17A and 17B illustrate examples of distributions of homopolymersignal values for MSS/Normal and MSI-High and in silico control.

FIGS. 18A and 18B illustrate examples of distributions of homopolymersignal values for MSS/Normal for on-chip control and in silico controlwhere there is low variability in chips and runs.

FIGS. 19A and 19B illustrate examples of distributions of homopolymersignal values for MSS/Normal for on-chip control and in silico controlwhere there is high variability in chips and runs.

FIG. 20 gives examples of synthetic calibration control sequences and areference sequence from hg19.

FIGS. 21A and 21B illustrate examples of distributions of HP signalvalues for SCC sequence reads for a group of SCC markers and adistribution HP signal values for sequence reads of a test sample at agiven marker.

FIG. 22A illustrates an example of a polynomial fit to generate a fittedcalibration curve for the examples shown in FIGS. 21A and 21B.

FIG. 22B shows an example of the correction of the distribution of theHP signal values for the test sample at the marker for the examples ofFIGS. 21A and 21B.

DETAILED DESCRIPTION

In accordance with the teachings and principles embodied in thisapplication, new methods, systems and non-transitory machine-readablestorage medium are provided to evaluate MSI status based on differenttypes of MSI markers, such as for long homopolymers and STRs. Furtherteachings provide for determining MSI status using tumor only samples.In some embodiments, the methods described herein may allow for tens tohundreds of MSI markers to be evaluated. Primers may be targeted toamplify MSI marker regions of interest. MSI marker regions may includerepeat regions, such as long homopolymers and other short tandem repeats(STRs).

In some embodiments, MSI marker regions having longer repeat regions aremore sensitive to MSI than shorter ones. In some embodiments, markerswith a smaller repeat unit in the repeat region are more sensitive toMSI than markers with longer repeat units. An observed behavior in longhomopolymers for MSI-H samples is a shorter homopolymer length than fornormal samples or a mix of shorter lengths and normal lengths in thesequence reads.

In various embodiments, DNA (deoxyribonucleic acid) may be referred toas a chain of nucleotides consisting of 4 types of nucleotides; A(adenine), T (thymine), C (cytosine), and G (guanine), and that RNA(ribonucleic acid) is comprised of 4 types of nucleotides; A, U(uracil), G, and C. Certain pairs of nucleotides specifically bind toone another in a complementary fashion (called complementary basepairing). That is, adenine (A) pairs with thymine (T) (in the case ofRNA, however, adenine (A) pairs with uracil (U)), and cytosine (C) pairswith guanine (G). When a first nucleic acid strand binds to a secondnucleic acid strand made up of nucleotides that are complementary tothose in the first strand, the two strands bind to form a double strand.In various embodiments, “nucleic acid sequencing data,” “nucleic acidsequencing information,” “nucleic acid sequence,” “genomic sequence,”“genetic sequence,” or “fragment sequence,” or “nucleic acid sequencingread” denotes any information or data that is indicative of the order ofthe nucleotide bases (e.g., adenine, guanine, cytosine, andthymine/uracil) in a molecule (e.g., whole genome, whole transcriptome,exome, oligonucleotide, polynucleotide, fragment, etc.) of DNA or RNA.

In various embodiments, a “polynucleotide”, “nucleic acid”, or“oligonucleotide” refers to a linear polymer of nucleosides (includingdeoxyribonucleosides, ribonucleosides, or analogs thereof) joined byinternucleosidic linkages. Typically, a polynucleotide comprises atleast three nucleosides. Usually oligonucleotides range in size from afew monomeric units, e.g. 3-4, to several hundreds of monomeric units.Whenever a polynucleotide such as an oligonucleotide is represented by asequence of letters, such as “ATGCCTG,” it will be understood that thenucleotides are in 5′->3′ order from left to right and that “A” denotesdeoxyadenosine, “C” denotes deoxycytidine, “G” denotes deoxyguanosine,and “T” denotes thymidine, unless otherwise noted. The letters A, C, G,and T may be used to refer to the bases themselves, to nucleosides, orto nucleotides comprising the bases, as is standard in the art.

The term “allele” as used herein refers to a genetic variationassociated with a gene or a segment of DNA, i.e., one of two or morealternate forms of a DNA sequence occupying the same locus.

The term “locus” as used herein refers to a specific position on achromosome or a nucleic acid molecule. Alleles of a locus are located atidentical sites on homologous chromosomes.

As used herein, the terms “adapter” or “adapter and its complements” andtheir derivatives, refers to any linear oligonucleotide which can beligated to a nucleic acid molecule of the disclosure. Optionally, theadapter includes a nucleic acid sequence that is not substantiallycomplementary to the 3′ end or the 5′ end of at least one targetsequences within the sample. In some embodiments, the adapter issubstantially non-complementary to the 3′ end or the 5′ end of anytarget sequence present in the sample. In some embodiments, the adapterincludes any single stranded or double-stranded linear oligonucleotidethat is not substantially complementary to an amplified target sequence.In some embodiments, the adapter is substantially non-complementary toat least one, some or all of the nucleic acid molecules of the sample.In some embodiments, suitable adapter lengths are in the range of about10-100 nucleotides, about 12-60 nucleotides and about 15-50 nucleotidesin length. An adapter can include any combination of nucleotides and/ornucleic acids. In some aspects, the adapter can include one or morecleavable groups at one or more locations. In another aspect, theadapter can include a sequence that is substantially identical, orsubstantially complementary, to at least a portion of a primer, forexample a universal primer. In some embodiments, the adapter can includea barcode or tag to assist with downstream cataloguing, identificationor sequencing. In some embodiments, a single-stranded adapter can act asa substrate for amplification when ligated to an amplified targetsequence, particularly in the presence of a polymerase and dNTPs undersuitable temperature and pH.

As used herein, “DNA barcode” or “DNA tagging sequence” and itsderivatives, refers to a unique short (e.g., 6-14 nucleotide) nucleicacid sequence within an adapter that can act as a ‘key’ to distinguishor separate a plurality of amplified target sequences in a sample. Forthe purposes of this disclosure, a DNA barcode or DNA tagging sequencecan be incorporated into the nucleotide sequence of an adapter.

In some embodiments, the disclosure provides for amplification ofmultiple target-specific sequences from a population of target nucleicacid molecules. In some embodiments, the method comprises hybridizingone or more target-specific primer pairs to the target sequence,extending a first primer of the primer pair, denaturing the extendedfirst primer product from the population of nucleic acid molecules,hybridizing to the extended first primer product the second primer ofthe primer pair, extending the second primer to form a double strandedproduct, and digesting the target-specific primer pair away from thedouble stranded product to generate a plurality of amplified targetsequences. In some embodiments, the digesting includes partial digestingof one or more of the target-specific primers from the amplified targetsequence. In some embodiments, the amplified target sequences can beligated to one or more adapters. In some embodiments, adapters caninclude one or more DNA barcodes or tagging sequences. In someembodiments, amplified target sequences once ligated to an adapter canundergo a nick translation reaction and/or further amplification togenerate a library of adapter-ligated amplified target sequences.

In some embodiments, the methods of the disclosure include selectivelyamplifying target sequences in a sample containing a plurality ofnucleic acid molecules and ligating the amplified target sequences to atleast one adapter and/or barcode. Adapters and barcodes for use inmolecular biology library preparation techniques are well known to thoseof skill in the art. The definitions of adapters and barcodes as usedherein are consistent with the terms used in the art. For example, theuse of barcodes allows for the detection and analysis of multiplesamples, sources, tissues or populations of nucleic acid molecules permultiplex reaction. A barcoded and amplified target sequence contains aunique nucleic acid sequence, typically a short 6-15 nucleotidesequence, that identifies and distinguishes one amplified nucleic acidmolecule from another amplified nucleic acid molecule, even when bothnucleic acid molecules minus the barcode contain the same nucleic acidsequence. The use of adapters allows for the amplification of eachamplified nucleic acid molecule in a uniformed manner and helps reducestrand bias. Adapters can include universal adapters or proprietyadapters both of which can be used downstream to perform one or moredistinct functions. For example, amplified target sequences prepared bythe methods disclosed herein can be ligated to an adapter that may beused downstream as a platform for clonal amplification. The adapter canfunction as a template strand for subsequent amplification using asecond set of primers and therefore allows universal amplification ofthe adapter-ligated amplified target sequence. In some embodiments,selective amplification of target nucleic acids to generate a pool ofamplicons can further comprise ligating one or more barcodes and/oradapters to an amplified target sequence. The ability to incorporatebarcodes enhances sample throughput and allows for analysis of multiplesamples or sources of material concurrently.

In this application, “reaction confinement region” generally refers toany region in which a reaction may be confined and includes, forexample, a “reaction chamber,” a “well,” and a “microwell” (each ofwhich may be used interchangeably). A reaction confinement region mayinclude a region in which a physical or chemical attribute of a solidsubstrate can permit the localization of a reaction of interest, and adiscrete region of a surface of a substrate that can specifically bindan analyte of interest (such as a discrete region with oligonucleotidesor antibodies covalently linked to such surface), for example. Reactionconfinement regions may be hollow or have well-defined shapes andvolumes, which may be manufactured into a substrate. These latter typesof reaction confinement regions are referred to herein as microwells orreaction chambers, and may be fabricated using any suitablemicrofabrication techniques. Reaction confinement regions may also besubstantially flat areas on a substrate without wells, for example.

A plurality of defined spaces or reaction confinement regions may bearranged in an array, and each defined space or reaction confinementregions may be in electrical communication with at least one sensor toallow detection or measurement of one or more detectable or measurableparameter or characteristics. This array is referred to herein as asensor array. The sensors may convert changes in the presence,concentration, or amounts of reaction by-products (or changes in ioniccharacter of reactants) into an output signal, which may be registeredelectronically, for example, as a change in a voltage level or a currentlevel which, in turn, may be processed to extract information about achemical reaction or desired association event, for example, anucleotide incorporation event. The sensors may include at least onechemically sensitive field effect transistor (“chemFET”) that can beconfigured to generate at least one output signal related to a propertyof a chemical reaction or target analyte of interest in proximitythereof. Such properties can include a concentration (or a change inconcentration) of a reactant, product or by-product, or a value of aphysical property (or a change in such value), such as an ionconcentration. An initial measurement or interrogation of a pH for adefined space or reaction confinement regions, for example, may berepresented as an electrical signal or a voltage, which may bedigitalized (e.g., converted to a digital representation of theelectrical signal or the voltage). Any of these measurements andrepresentations may be considered raw data or a raw signal.

In various embodiments, the phrase “base space” refers to arepresentation of the sequence of nucleotides. The phrase “flow space”refers to a representation of the incorporation event ornon-incorporation event for a particular nucleotide flow. For example,flow space can be a series of values representing a nucleotideincorporation event (such as a one, “1”) or a non-incorporation event(such as a zero, “0”) for that particular nucleotide flow. Nucleotideflows having a non-incorporation event can be referred to as emptyflows, and nucleotide flows having a nucleotide incorporation event canbe referred to as positive flows. It should be understood that zeros andones are convenient representations of a non-incorporation event and anucleotide incorporation event; however, any other symbol or designationcould be used alternatively to represent and/or identify these eventsand non-events. In particular, when multiple nucleotides areincorporated at a given position, such as for a homopolymer stretch, thevalue can be proportional to the number of nucleotide incorporationevents and thus the length of the homopolymer stretch.

FIG. 1 illustrates examples of nucleic acid sequences having MSI markerregions and flank regions. The MSI marker for this example is a longhomopolymer as shown in the top sequence. A left flank and right flankare adjacent to the long homopolymer. A long homopolymer may have alength of 8 or more bases, for example. The flanks may include 14-15bases, for example. The homopolymer region in the center sequence hasthe same length as that of a control, or reference, sequence and can bean example of MSI-Low (MSI-L) status. MSI-Low status is also referred toherein as microsatellite stable (MSS) and MSS/Normal. The bottomsequence has a much shorter homopolymer region and can be an example ofMSI-H status. Shorter homopolymer length can be an indicator of MSI-Hstatus.

FIG. 2 illustrates an example of a sequence read having an STR region.The sequence read includes an STR region surrounded by a left flank anda right flank. The STR region may include repeats of a short sequence ofbases, or repeat sequence. The repeat sequence may have 2 bases(dinucleotides), 3 bases (trinucleotides) or more bases. The STR regionis adjacent to a non-repetitive sequence of bases of the left flank anda non-repetitive sequence of bases of the right flank. The left flank isadjacent to the 5′ barcode adapter and the right flank is adjacent tothe 3′ barcode adapter.

In some embodiments, an aligned BAM file including aligned sequence readinformation is provided to a processor for analyzing the alignedsequence reads corresponding to marker regions for determining an MSIscore for the sample. The sequence read may include a sequence of basesof a left flank, a sequence of bases of a right flank and a repeatregion of bases positioned between a rightmost base of the left flankand a leftmost base of the right flank. The repeat region includesrepeats of a single base for a homopolymer or a repeated sequence ofbases for an STR. In some embodiments, portions of the aligned sequencereads may correspond to marker regions for long homopolymers and/orSTRs. In some embodiments, the left and, optionally, the right flanksequence may be identified by alignment to a reference sequence using aSmith Waterman alignment algorithm or other suitable mapping algorithm.The identification of the repeat sequence and flank regions for use withthe present teachings may include one or more features described in U.S.Pat. Appl. Publ. No. 2018/0181707, published Jun. 28, 2018, incorporatedby reference herein in its entirety.

FIG. 3 is a block diagram of a method of determining an MSI score for asample, according to an exemplary embodiment. The homopolymer classifieranalyzes flow space signal measurements of the sequence readscorresponding to marker regions having long homopolymers to produce permarker scores for the long homopolymers. The STR classifier analyzesaligned sequence reads corresponding to marker regions having STRsequences to produce per marker scores for the STR sequences. Thecombiner adds per marker scores that meet threshold and coveragecriteria to produce a total MSI score for the sample.

In some embodiments, a flow space signal measurement represents a signalamplitude or intensity measured in response to an incorporation ornon-incorporation of a flowed nucleotide by sample nucleic acids inmicrowells of a sensor array. For an incorporation event, the signalamplitudes depend on the number of bases incorporated at one flow. Forhomopolymers, the signal amplitudes increase with increasing homopolymerlength. Flow space signal measurements are described in more detailbelow.

In some embodiments, the homopolymer classifier determines a score permarker for the aligned sequence reads corresponding to a marker regionhaving a long homopolymer by the following steps:

-   -   A.1. Identify sequence reads having the left flank sequence for        the target homopolymer corresponding to the marker.    -   A.2. For each sequence read, calculate a sum of M flow space        signal measurements corresponding to M nucleotide flows of the        sequence of flows having the same nucleotide type as the target        homopolymer to form a homopolymer (HP) signal value for the        sequence read.    -   A.3. Calculate a histogram of HP signal values for the sequence        reads in the forward direction and a histogram of HP signal        values for the sequence reads in the reverse direction.    -   A.4. Identify features of the HP signal histograms for the        forward and reverse directions to be used for evaluating the MSI        status corresponding to the marker.    -   A.5. Calculate a score per marker using the features of the HP        signal histograms.

FIG. 4 shows an example of superimposed flow space signal measurementsfor homopolymer sequence reads that are MSI-H and MSI-L, respectively.The MSI-L sequence read contains a longer homopolymer of the base A thanthat of MSI-H sequence read. The flow space signal measurement for thelonger homopolymer has greater amplitude because more A's areincorporated in response to the nucleotide flow. The flow space signalmeasurement for the MSI-H sequence read has lower amplitude because thehomopolymer is shorter and therefore fewer A's are incorporated inresponse to the nucleotide flow. The flow space signal measurements tothe left of the homopolymer A indicate nucleotide incorporations for theleft flank sequence (AGGATCTT). The flow space signal measurements tothe right of the homopolymer A indicate nucleotide incorporations forthe right flank sequence (TGCTGCAT).

In some embodiments, a number of flow space signals measurements M to beadded may be determined for step A.2. For sequencing by synthesis, thesequence of flows may comprise repeats of a flow order of nucleotides,such as T-A-C-G, for example. During sequencing of a homopolymer region,multiple incorporations of a particular nucleotide may occur overseveral repeats of the flow order. For example, the nucleotide A may beincorporated over several repeats of the flow order when sequencing along homopolymer. The flow space signal measurement values for theincorporations of A may decrease over subsequent repeats until the endof the homopolymer is reached and incorporations of differentnucleotides begin for the flank region. The number of flow space signalmeasurements M corresponding to the homopolymer region may be determinedby at least one of the following:

-   -   A.a) Determine the flow space signal measurements corresponding        to the same nucleotide in the flow order having values above a        threshold signal level. Flow space signal measurement values may        decrease over subsequent flows of the same nucleotide in        subsequent repeats of the flow order.    -   A.b) Determine the flow space signal measurements where one or        more nucleotides corresponding to the flank sequence is/are        incorporated. The first nucleotide corresponding to the flank        sequence will be different from that of the homopolymer region.        In some embodiments, the number of flow space signal        measurements in step A.a) may indicate M. In some embodiments,        applying steps A.a) and A.b) may determine M based on the number        flow space signal measurements for the homopolymer before the        beginning of the flank sequence.

FIGS. 5A, 5B, 5C, 5D, 6A and 6B show examples of histograms of HP signalvalues for tumor and normal samples. The histograms are the results ofthe above step A.3 for sequence reads of long homopolymers correspondingto marker BAT25 (FIGS. 5A and 5B), the marker BAT26 (FIGS. 5C and 5D)and the marker NR21 (FIGS. 6A and 6B). The x-axis gives the HP signalvalues resulting from summing the flow space signal measurements, as instep A.3. The y-axis gives the number of sequence reads. Histograms ofHP signal values for the sequence reads in the forward direction areabove the x-axis and histograms of HP signal values for the sequencereads in the reverse direction are below the x-axis. The histograms ofHP signal values for the tumor samples, given in FIGS. 5A, 5C and 6A,show several differences from those of the normal samples, given inFIGS. 5B, 5D and 6B. The differences for the HP signal values for thetumor samples compared to the normal samples include shifts to the leftindicating lower HP signal values due to shortened homopolymers, widerranges and multi-modal distributions.

In some embodiments, the features of the HP signal histogram may bebased on the mean and standard deviation of the HP signal values. Thescore per marker using these features may be calculated as follows:

-   -   A.i) Calculate the mean and standard deviation (std) of the HP        signal values for a control to form mean_(SAMP) and std_(CTL).    -   A.ii) Calculate the mean and standard deviation of the HP signal        values for a sample to form mean_(SAMP) and std_(SAMP).    -   A.iii) Calculate a mean        difference=(mean_(CTL)−mean_(SAMP))/std_(CTL).    -   A.iv) Calculate a std difference=std_(SAMP)−std_(CTL).

In some embodiments, a first feature, f₁ is the mean differencecalculated in step A.iii) above and a second feature f₂ is the stddifference calculated in A.iv) above. In some embodiments, the score permarker may be calculated by a weighted sum:

Score per marker=a ₁ f ₁ +a ₂ f ₂  (1)

where a₁ and a₂ are weights.

In some embodiments, a sigmoid function S may be applied to the featuresto give S(f₁) and S(f₂). The sigmoid function may be useful forfiltering out noise. In some embodiments, the score per marker may becalculated by a weighted sum of the sigmoid functions of the features:

Score per marker=a ₁ S(f ₁)+a ₂ S(f ₂)  (2)

In some embodiments, the sigmoid function S(x) is given by

S(x)=C1/(1+exp(−C2*(x−C3)))  (3)

where C1 determines the maximum height of the sigmoid, C3 determines theshift and C2 determine the slope of the sigmoid. FIG. 7 shows an exampleof a sigmoid function.

In some embodiments, the control measurements may be obtained bysequencing an on-chip control sample. For example, the on-chip controlsample may be provided by control genomic DNA from CEPH Individual1347-02 (ThermoFisher Scientific catalog no. 403062) or Raji genomic DNA(part of TaqMan™ RNase P Detection Reagents Kit, ThermoFisher Scientificcatalog no. 4316831). In some embodiments, the on-chip control samplemay be sequenced in the same sequencing run as the test sample. Thesequence reads for the on-chip control sample corresponding to themarker regions of interest are analyzed as described above to determinethe HP signal values, mean_(CTL) and std_(CTL).

Advantages of using an on-chip control sample include providing accuratedeterminations of the mean_(CTL) and std_(CTL) for a given run thatcapture chip to chip variabilities and run to run variabilities in flowspace signal values due to variations in chip gain and chemicalbuffering behavior. The flow space signal measurements for the on-chipcontrol sample may be obtained during the same sequencing run as for thesample being tested, or test sample. The sequence reads for the on-chipcontrol sample having the target homopolymer are identified as in stepA.1 above. The HP signal values corresponding to the sequence reads forthe on-chip control sample are calculated as in step A.2 above. Themean_(CTL) and std_(CTL), for the HP signal values for the on-chipcontrol sample are calculated as in step A.i). The score per marker isdetermined using the mean difference for the sample and the on-chipcontrol as determined in step A.iii) and the std difference determinedby step A.iv) above. Since the mean_(CTL) and std_(CTL) are calculatedfor the same run as the mean_(SAMP) and std_(SAMP), chip to chipvariabilities and run to run variabilities may not affect their values.

FIGS. 16A and 16B illustrate examples of distributions of homopolymersignal values for on-chip control, MSS/Normal and MSI-High. Thedistributions represent idealized histograms. FIGS. 16A and 16B show anexample on-chip control distribution having mean_(CTL)=8000 andstd_(CTL)=σ. FIG. 16A shows an example an MSS/Normal distribution havinga mean_(SAMP1) a distance of D1 from mean_(CTL), whereD1=(mean_(CTL)−mean_(SAMP1)). FIG. 16B shows an example MSI-Highdistribution having a mean_(SAMP2) a distance of D2 from mean_(CTL),where D2=(mean_(CTL)−mean_(SAMP2)).

However, the on-chip control sample occupies valuable space on the chip,in addition to the space occupied by the sample. In the case of highermultiplexing or a large panel, an increased number of markers in thecontrol sample may reduce the coverages for some of the markers to belowa minimum coverage, so that the scores for those markers may not beestimated.

In some embodiments, sequencing the on-chip control in one sequencingrun can provide the mean_(CTL) and std_(CTL) that is stored for use asan in silico control for sequencing runs having no on-chip controlsample. In this case, the step A.i) above is omitted and the storedvalues for the mean_(CTL) and std_(CTL) are used in steps iii) and iv).The in silico control positions may be measured using on-chip controlsfrom previous runs having similar conditions, such as type of chip,chemistry and flow order. For detecting MSI status using a tumor-onlyanalysis, stored values for mean_(CTL) and std_(CTL) can be used insteadof sequencing an on-chip control from a normal sample to determine theper marker scores. The total MSI score can be determined by combiningthe per marker scores using the tumor-only analysis.

FIGS. 17A and 17B illustrate examples of distributions of homopolymersignal values for MSS/Normal and MSI-High and in silico control. FIGS.17A and 17B show an example where the in silico control mean value, insilico mean_(CTL)=8000. For example, the value for silico mean_(CTL) mayhave been determined from a previous run using on-chip control asdescribed with respect to FIGS. 16A and 16B. FIG. 17A shows an exampleMSS/Normal distribution having a mean_(SAMP1) a distance of D1 from insilico mean_(CTL), where D1=(in silico mean_(CTL)−mean_(SAMP1)). FIG.17B shows an example MSI-High distribution having a mean_(SAMP2) adistance of D2 from in silico mean_(CTL), where D2=(in silicomean_(CTL)−mean_(SAMP2)).

An advantage of using the in silico control is that space on the chipfor an on-chip control sample is not required. Another advantage is thatin silico control is not affected by multiplexing or insufficientcoverage of marker regions. This is because the in silico controlvalues, e.g. mean_(CTL) and std_(CTL), corresponding to each of themarker regions are stored from a previous run. However, the in silicocontrol does not capture run variabilities. The run variabilities mayimpact the calculated differences (mean_(CTL)−mean_(SAMP)) and(std_(CTL)−std_(SAMP)), possibly reducing the accuracy of the per markerscore.

FIGS. 18A and 18B illustrate examples of distributions of homopolymersignal values for MSS/Normal for on-chip control and in silico controlwhere there is low variability in chips and runs. FIG. 18A shows anexample MSS/Normal distribution having a mean_(SAMP) a distance of D1from mean_(CTL), where D1=(mean_(CTL)−mean_(SAMP)), where mean_(CTL)corresponds to an on-chip control sample. FIG. 18B shows an exampleMSS/Normal distribution having a mean_(SAMP) a distance of D1 frommean_(CTL), where D1=(mean_(CTL)−mean_(SAMP)), where mean_(CTL)corresponds to an in silico control calculated in a previous run. Forlow variability from run to run or from chip to chip, the in silicocontrol can provide results for D1=(mean_(CTL)−mean_(SAMP)) that areconsistent with those with on-chip control.

FIGS. 19A and 19B illustrate examples of distributions of homopolymersignal values for MSS/Normal for on-chip control and in silico controlwhere there is high variability in chips and runs. FIG. 19A shows anexample MSS/Normal distribution having a mean_(SAMP) a distance of D1from mean_(CTL), where D1=(mean_(CTL)−mean_(SAMP)), where mean_(CTL)corresponds to an on-chip control sample. In FIG. 19A, both curves areshifted to lower HP signal values compared with those of FIG. 18A,however the distance D1 is the same for both FIGS. 18A and 19A. The onchip control is able to compensate for high variability from chip tochip and run to run. FIG. 19B shows an example MSS/Normal distributionhaving a mean_(SAMP) a distance of D1 from mean_(CTL), whereD1=(mean_(CTL)−mean_(SAMP)), where mean_(CTL) corresponds to the same insilico control FIG. 18B. A shift in the MSS/Normal distribution due tovariability of the chip or run, results in the D1 value of FIG. 19B thatis significantly different than that of the example of FIG. 18B. Theincrease in D1 can produce an erroneous result in the mean differencecalculation in step A.iii) above and an erroneous score per marker.

In some embodiments, the in silico control values for a givenhomopolymer calculated in a previous sequencing run may be modified in ausing flow space signal measurements obtained in a current sequencingrun. A combination of on-chip and in silico control is referred toherein as hybrid control. In hybrid control, the marker regions of anon-chip sample with sufficient coverage, for example at least 50sequence reads, are used to estimate parameters of a transformation thatcan be applied to the in silico features to produce modified controlfeatures for a current run. In some embodiments, the test sample mayinclude regions having monomorphic homopolymers. Monomorphichomopolymers have lengths that are stable in the human genome. Thesemonomorphic homopolymer regions may be included in the gene panel forthe test sample. When the on-chip sample is the test sample havingmonomorphic homopolymer regions, there may be no additional on-chipcontrol sample required for the sequencing run. In other embodiments,there may be an on-chip control sample. In this case, certain markersfor homopolymer regions in the on-chip control sample are selected ifthey have sufficient coverage, e.g. at least 50 sequence reads. The flowspace signal measurements from the selected homopolymer marker regionsare used to determine parameters of the transformation.

FIG. 14 is a block diagram of a method for hybrid control, according toan exemplary embodiment. The in silico features include the controlvalues for one or more homopolymer marker regions calculated in aprevious sequencing run. These in silico features may include themean_(CTL) values and std_(CTL) values calculated for one or morehomopolymer marker regions in an on-chip control sample in a previousrun. In a first step, the parameters of a transformation T arecalculated using flow space signal measurements corresponding to aselected set of homopolymer marker regions from the current run. The setof homopolymer marker regions from the current run may be selected basedon sufficient coverage of the homopolymer region by the sequencingreads, for example 50 or more sequencing reads. The HP signal valuescorresponding to the sequence reads for the selected set homopolymermarker regions are calculated as in step A.2 above. The mean andstandard deviation, mean_(SEL) and std_(SEL), for the HP signal valuesfor each homopolymer in the selected set of homopolymer marker regionsare calculated as in step A.i). The values for mean_(SEL) for theselected set of homopolymers and the corresponding values for the samehomopolymers in silico mean_(CTL) values determined from the previoussequencing run may be used to determine the parameters of thetransformation T. For example, the transformation T may comprise apolynomial function of the in silico mean_(CTL) values. The parametersmay be determined that minimize the error between the measuredmean_(SEL) and the estimated mean_(SEL) values, where

Estimated mean_(SEL) =T[in silico mean_(CTL)]  (4)

Error=Estimated mean_(SEL)−Measured mean_(SEL)  (5)

For example, the parameters of T may be determined based on a minimummean squared error criterion to achieve a linear or polynomial fit. Insome embodiments, the set of homopolymer marker regions may be selectedfrom the test sample having monomorphic homopolymer regions withsufficient coverage. Monomorphic homopolymers having homopolymer lengthsbetween 8 and 14 are more likely to have sufficient coverage. Forexample, selecting monomorphic homopolymers in the test sample havinglengths of 8, 10, 12 and 14 may be used to calculate the measuredmean_(SEL) for each of the selected homopolymer lengths.

In some embodiments, the set of homopolymer marker regions may beselected from an on-chip control sample, where the selected homopolymerregions have sufficient coverage. The HP signal values corresponding tothe sequence reads for selected homopolymer regions of the on-chipcontrol sample may be calculated as in step A.2 above. The mean_(SEL)and std_(SEL) of the HP signal values for the selected homopolymerregions of the on-chip control sample may be calculated as in step A.i).The measured mean_(SEL) may be used to determine the parameters of T, asdescribed above with respect to FIG. 14 .

Returning to FIG. 14 , once the parameters of the transformation T havebeen determined, the transformation T can be applied to in silicofeatures to produce estimated on-chip features for homopolymer markerregions with low coverage. The homopolymer regions with low coverage,e.g. fewer than 50 sequencing reads, have insufficient coverage fordetermination of control values mean_(CTL) values and std_(CTL) based onsequencing reads from a current run. For example, a hybrid control valuefor the mean, [Mean_(CNTL)]_(HYB), for a homopolymer marker regionhaving a low coverage can be calculated using the in silico mean_(CNTL)as follows:

[mean_(CNTL)]_(HYB) =T[mean_(CNTL)]_(IN SILICO)  (6)

The hybrid control value [mean_(CNTL)]_(HYB) may be used for mean_(CNTL)in step A.iii) above to calculate the mean difference. It has beenobserved that the standard deviations for the selected homopolymermarker regions do not vary significantly from those of the in silicocontrol. Determining parameters for a transformation and applying atransformation to the standard deviation values for the in silicocontrol may be optional or not needed.

In some embodiments, synthetic calibration control strands may beprovided along with the test sample for the sequencing run. Thesynthetic calibration control (SCC) strands may include synthetic DNAstrands having homopolymers of known lengths. The synthetic DNA strandmay be structured to have a homopolymer of the desired length, a leftflank region on the 5′ side of the homopolymer, a right flank region onthe 3′ side of the homopolymer, a 5′ primer adjacent to the left flankregion and a 3′ primer adjacent to the right flank region. The primersmay be targeted to amplify MSI marker regions of interest in the MSIpanel content. In some embodiments, the SCC may not correspond to theMSI markers targeted in the panel and may be unique sequences that areidentified by a unique reference sequence. The synthetic homopolymersmay be structured to have base types and lengths relevant for the markerregions of interest. For example, if the MSI panel includes markerregions of interest that include homopolymers of A and homopolymers ofT, the synthetic calibration control strands may include homopolymers ofA and homopolymers of T. For example, the lengths of the synthetichomopolymers in the SCC strands may be 13 to 30 bases. In someembodiments, there may be 3-4 different lengths of homopolymers for amarker region of interest. For example, for 9 different MSI markers ofinterest in a MSI panel, the SCC amplicons for each marker may include3-4 different HP lengths. Examples of other numbers of HP lengths foreach marker include 1, 1-2 or 2-3.

In some embodiments, the synthetic calibration control may include afirst tag sequence in the left flank region and a second tag sequence inthe right the right flank region. The first tag sequence is substitutedfor the sequence of bases that occur in the reference sequence atspecific locations in the left flank region. The second tag sequence issubstituted for the sequence of bases that occur in the referencesequence at specific locations in the right flank region. The tagsequences may be 3-4 bases in length. The tag sequences allow the SCCsequence reads to be identified in the aligned sequence read informationprovided in an aligned BAM file, for example, after alignment with thereference sequence. The tag sequence lengths may be 3-4 so that themapping of the sequence reads to the reference sequence may providealigned SCC sequence reads, although there may be mismatches between thetag sequences and the corresponding locations in the reference sequence.

FIG. 20 gives examples of synthetic calibration control sequences and areference sequence from hg19. In this example, the marker region ofinterest in the reference sequence has an HP length of 18 A's. The firstexemplary SCC sequence has an HP length of 18 A's. The second exemplarySCC sequence has an HP length of 14 A's. The third exemplary SCCsequence has an HP length of 22 A's. These exemplary SCC sequences eachhave a tag sequence in the left flank region and a tag sequence in theright flank region. The tag sequences substitute 3 or 4 bases for basesin the corresponding 3 or 4 positions in the reference sequence. Forexample, for “CATT” in the left flank of the reference sequence, thefirst exemplary SCC sequence may substitute the tag “GATG” in its leftflank. For example, for the “AAT” in the right flank of the referencesequence, the first exemplary SCC sequence may substitute the tag “TAC”in its right flank. For example, the second exemplary SCC sequence, thetag “GAT” may substitute for reference's “ATT” in the left flank and thetag “CGT” may substitute for the reference's “AAT” in the right flank.For example, the third exemplary SCC sequence, the tag “TGA” maysubstitute for reference's “ATT” in the left flank and the tag “GCG” maysubstitute for the reference's “AAT” in the right flank.

In some embodiments, Invitrogen GeneArt Gene Synthesis available fromThermoFisher Scientific, or DNA synthesis from other vendors, may beused to generate the synthetic DNA for the SCC sequences. The syntheticDNA for the SCC sequences may be amplified together with the test sampleDNA in the presence of a primer pool targeting the MSI markers ofinterest. The SCC amplicons and the sample amplicons may be sequenced asdescribed below to form sequence reads corresponding to the SCCs and thesample. The sequence reads may be mapped to the reference sequence toform aligned sequence read information for an aligned BAM file.

In some embodiments, the sequence reads for the target homopolymercorresponding to the marker are identified as described above for thehomopolymer classifier in step A.1. The left flank region and rightflank region of the aligned sequence reads are analyzed to detect thepresence of tag sequences. If the tag sequences are detected, theparticular tag sequences identify the corresponding SCC sequence. If thetag sequences are not detected, the sequence read may correspond to thesample being tested. For each SCC sequence read, calculate a sum of Mflow space signal measurements corresponding to M nucleotide flows ofthe sequence of flows having the same HP nucleotide type as the targethomopolymer to form a HP signal value for the SCC sequence read, as instep A.2 above. As in step A.3 above, calculate a histogram of HP signalvalues for the SCC sequence reads in the forward direction and ahistogram of HP signal values for the SCC sequence reads in the reversedirection. As in step A.i) above, calculate the means_(sec) andstd_(sec) for the histogram of HP signal values for each of the SCCsequence reads.

FIGS. 21A and 21B illustrate examples of distributions of HP signalvalues for SCC sequence reads for a group of SCC markers and adistribution HP signal values for sequence reads of a test sample at agiven marker. The distributions of HP signal values for the SCC sequencereads correspond to various known HP lengths. The position on the x-axisof each peak value corresponds to the mean value. FIG. 21A showsexamples of baseline distributions of HP signal values of SCC sequencereads, including distributions 2101, 2102, 2103, 2104, 2105, 2106 and2107. The baseline in silico control reference value is also indicated.The j-th baseline mean value, mean_(BASEELINE)(j), of the distributionsfor the j-th SCC and the in silico control value can be stored in aconfiguration file. FIG. 21B shows how a low gain sequencing run canaffect the distributions of HP signal values for the SCCs and the testsample. For the low gain sequencing run, the distributions of HP signalvalues are non-uniformly shifted, where higher values are shifted morethan lower values. For example, the distributions 2111, 2112, 2113,2114, 2115, 2116 and 2117 are shifted relative to the correspondingdistributions 2101, 2102, 2103, 2104, 2105, 2106 and 2107 in FIG. 21A.The distribution of HP signal values for the marker 1 are shiftedfurther from the in silico control value, which could lead to anerroneous MSI score for that marker.

In some embodiments, a correction can be applied to HP signal values ofthe distributions corresponding to the SCCs and the distributioncorresponding to the test sample at the marker to correct for thedistortion in a current run. The baseline mean value,mean_(BASELINE)(j), for each j-th SCC in a baseline run can be modeledas a polynomial function of the mean value, mean_(CURRENT)(j) for acurrent run. FIG. 22A illustrates an example of a polynomial fit togenerate a calibration curve for the examples shown in FIGS. 21A and21B. The x-axis gives the HP signal values for the current run to becalibrated. The y-axis gives the HP signal values for the baseline. Thecircles indicate the mean_(CURRENT)(j) on the x-axis mapped to themean_(BASELINE)(j) on the y-axis for the j-th SCC. A polynomial functiony=f(x) may be fitted using various approaches, such as a least squaresmethod, to generate a calibration curve, where,

mean_(BASELINE)(j)=f[mean_(CURRENT)(j)]  (7)

for the HP lengths of the SCCs. The fitted polynomial function can thenbe applied to the mean of the HP signal values for the test sample foreach homopolymer marker, mean_(MARKER). The polynomial function can haveany suitable order, such as first order (linear function), second order(quadratic function) or a higher order. The polynomial function maps themean_(MARKER) value to a corrected mean_(MARKER) value, where,

corrected mean_(MARKER) =f[mean_(MARKER)]  (8)

The dashed arrows in FIG. 22A illustrate this mapping by the polynomialfunction. FIG. 22B shows an example of the correction of thedistribution of the HP signal values for the test sample at the markerfor the examples of FIGS. 21A and 21B. The corrected distribution of HPsignal values for the test sample at the marker is restored to theposition in the baseline plot of FIG. 21A. The distance between thecorrected mean_(MARKER) value and the in silico control value is thesame as in the baseline plot of FIG. 21A. The calculation of the meandifference, as in step A.iii) above, using the corrected mean_(MARKER)value and the in silico control value for mean_(CTL) is given by,

mean difference=(mean_(CTL)−corrected mean_(MARKER))/std_(CTL)  (9)

In some embodiments, a method for determining a score per markercorresponding to a marker region having a long homopolymer usingsynthetic calibration controls may comprise the steps of a) generatingsynthetic calibration control (SCC) nucleic acid strands having knownhomopolymer portions with known lengths and tag sequences, b) amplifyingthe SCC nucleic acid strands and a nucleic acid test sample in thepresence of a primer pool targeting the MSI markers of interest toproduce a plurality of amplicons, c) sequencing the amplicons togenerate a plurality of sequence reads, d) mapping the sequence reads toa reference sequence, wherein the reference sequence includes the MSImarker regions of interest, for each marker region: e) identifying tagsequences in the SCC sequence reads, f) for each sequence read,calculate a sum of flow space signal measurements corresponding tonucleotide flows having the same nucleotide type as the targethomopolymer to form a homopolymer (HP) signal value for the sequenceread, g) generating a histogram of HP signal values for the sequencereads of the marker region, h) calculating a mean value for thehistogram of HP signal values for the SCC sequence reads correspondingto each homopolymer length, i) determining a polynomial function thatmaps the mean value corresponding to each HP length to baseline meanvalues, j) calculating a mean value of the histogram of HP signal valuesfor the test sample for each marker region to form a marker mean value,k) applying the polynomial function to each marker mean value to form acorrected marker mean value, l) calculating a difference between an insilico control mean value and the corrected marker mean value, m)calculating a standard deviation of the histogram of HP signal valuesfor the test sample for the marker region to form a marker standarddeviation, n) calculating a difference between the marker standarddeviation and an in silico standard deviation, and o) determining ascore for the marker based on mean difference and the standard deviationdifference.

In some embodiments, the STR classifier determines an MSI score permarker for the aligned sequence reads corresponding to a marker regionhaving dinucleotide repeats or STR by applying the following steps tosequence reads obtained by sequencing a tumor sample:

-   -   B.1) Identify sequence reads having the left flank sequence for        the target dinucleotide repeat or STR corresponding to the        marker.    -   B.2) For each sequence read in base space, count the number of        repeats of the repeated sequence of bases. Counts for individual        bases that do not form a complete repeat sequence are given to        the right of the decimal point. For example, a count of 10.2 for        a sequence read indicates 10 repeats of the complete repeat        sequence and 2 additional bases of a partial repeat.    -   B.3) Calculate a histogram of repeat lengths of the number        sequence reads versus the number of repeats.    -   B.4) Calculate a score per marker for the dinucleotide repeat or        STR based on features of the histograms.

FIGS. 8A and 8B show examples of histograms of repeat lengths for MSI-H(8A) and matched normal samples (8B) of dinucleotide repeatscorresponding to the marker LIMCH1. FIGS. 8C and 8D show examples ofhistograms of repeat lengths for MSI-H (8C) and matched normal samples(8D) of trinucleotide repeats corresponding to the marker VPS13A. Thex-axis gives the repeat length values. The y-axis gives the number ofsequence reads. Histograms of repeat lengths for the sequence reads inthe forward direction are above the x-axis and histograms of repeatlengths for the sequence reads in the reverse direction are below thex-axis. The histograms of repeat lengths for the MSI-H samples each showa second repeat length with a significant number of sequence reads whilethe normal samples each have one repeat length with a significant numberof sequence reads.

In some embodiments, a first feature may include the histogram binhaving the highest number of sequence reads and a second feature mayinclude the histogram bin having the second highest number of sequencereads. Using these features, a score per marker for the dinucleotiderepeat or STR may be calculated as follows:

-   -   B.a. Calculate a ratio of the second highest number of sequence        reads to the first highest number of sequence reads in the        histogram of repeat lengths.    -   B.b. Apply a sigmoid function to the ratio.    -   B.c. Multiply the output of the sigmoid function by a constant        to give the score per marker for the dinucleotide repeat or STR.    -   B.d. Repeat steps a, b and c for sequence reads on forward and        reverse strands.

In some embodiments, the multiplication by the constant may provide acommon range for the score per marker for a dinucleotide repeat or STRand the score per marker for a long homopolymer. The constant may bedetermined by comparing a first range of MSI scores per marker for longhomopolymers to a second range of MSI scores per marker for dinucleotiderepeats or STRs in a truth set of samples where MSI status is known. Theconstant may be based on a ratio of the first range to the second range.

The STR classifier method described above can determine an MSI score permarker for STR regions in a tumor-only analysis. The histogram of repeatlengths may be calculated and analyzed for sequence reads obtained fromtumor samples.

In some embodiments, a total score may be calculated based on the permarker scores, as follows:

-   -   B.I. Determine whether the sequence reads associated with the        marker have a coverage levels greater than or equal to a minimum        coverage level. For example a minimum coverage level may be 20        sequence reads.    -   B.II. Apply a threshold score to the score per marker calculated        for the forward sequence reads and the reverse sequence reads        for each of the markers and select the scores greater than or        equal to the threshold score. The threshold score may be set by        the user.    -   B.III. Sum the selected scores for the forward sequence reads        meeting the minimum coverage criterion to produce a summed score        for the forward sequence reads.    -   B.IV. Sum the selected scores for the reverse sequence reads        meeting the minimum coverage criterion to produce a summed score        for the reverse sequence reads.    -   B.V. Add the summed score for the forward sequence reads to the        summed score for the reverse sequence reads to produce a total        MSI score for the sample.    -   B.VI. If coverage levels for some markers do not meet the        minimum coverage level in step B.I, normalize the total MSI        score based on the number of markers having coverage levels        greater than or equal to the minimum coverage level to produce        the total MSI score for the sample. The normalization may be        calculated as follows:

TS_n=TS*(T/(T−N))  (10)

-   -    where TS_n is the normalized total MSI score, TS is the total        MSI score calculated in step B.V, T is the total number of        markers and N is the number of markers that have less than the        minimum coverage level in step B.I.

The total MSI score can be assigned to each sample using per marker MSIscores across multiple markers. The total MSI score can be used toevaluate MSI status. The total MSI score for a tumor only sample can beobtained when in silico control with stored values for mean_(CTL) andstd_(CTL) are used by the homopolymer classifier method instead ofsequencing an on-chip control from a normal sample. Thus, the MSI statuscan be evaluated with tumor only samples.

FIG. 9 gives an exemplary table of results of per marker scores andtotal MSI scores for several markers in six samples with known MSIstatus. The per marker scores and total MSI scores were determined bythe methods described herein. The substantial differences in total MSIscores between the MSI-H samples and MSI-L samples demonstrate theability of the total MSI score to discriminate between MSI-H and MSI-Lstatus.

FIG. 10 gives an exemplary table of results of testing of MSI statususing capillary electrophoresis (CE). FIG. 11 gives an exemplary tableof results of testing of MSI status using the total MSI score determinedby the NGS methods described herein. These exemplary results oforthogonal testing show that NGS results using the methods describedherein are concordant with sample designation of MSI status found by CE.

FIG. 15 gives a plot of exemplary results for MSI scores determinedusing on-chip control samples and hybrid control. The sequencing readdata were generated from 10×, 20×, 30× and 40× multiplexing applied tothe same biological sample in different runs. The MSI scores (y-axis)correspond to the total MSI scores, such as those given in the bottomline of FIG. 9 . The read coverage (x-axis) is given in number of readsfor the same sample resulting from multiple experiments. The coefficientof variation (CV) for the MSI scores is defined as the ratio of thestandard deviation to the mean of the MSI scores. In this example, theMSI scores resulting from using the targeted homopolymer regions ofon-chip control sample show variability ranging from MSI scores of 160to 220. The coefficient of variation for the on-chip control,CV_(ON-CHIP) is 11.4%. For the hybrid control, a set of the targetedhomopolymer marker regions of the on-chip control sample were selectedbased on coverage. The selected homopolymer marker regions were used todetermine hybrid control values [mean_(CNTL)]_(HYB) for the homopolymermarker regions having low coverage. The hybrid control values[mean_(CNTL)]_(HYB) were used in the MSI score computations describedabove. The MSI scores for the same samples computed using the hybridcontrol values show greater uniformity and are distributed over asmaller range of values. The coefficient of variation for the hybridcontrol, CV_(HYBRID) is 3.7%. These results show that the hybrid controlvalues improved the accuracy of the MSI scores by improving uniformityand reducing variability in results caused by lower coverage and run torun variability.

Information about MSI markers and applications is given in the followingpublications: R. Bonneville, M. A. Krook et al. Landscape ofMicrosatellite Instability Across 39 Cancer Types. JCO Precis Oncol.2017; J. Hempelmann, C. Lockwood et al. Microsatellite instability inprostate cancer by PCR or next-generation sequencing, Journal forImmunoTherapy of Cancer 20186:29; Y. Maruvka, K. Mouw et al. Analysis ofsomatic microsatellite indels identifies driver events in human tumors,Nature Biotechnology 35, 951-959; and Cortes-Ciriano, S. Lee et al. Amolecular portrait of microsatellite instability across multiplecancers, Nature Communications 8, 15180.

According to an exemplary embodiment, there is provided a method fordetecting microsatellite instability (MSI) in a sample, including: (1)receiving a plurality of nucleic acid sequence reads corresponding to aplurality of marker regions for MSI, wherein each of the sequence readsincludes a left flank sequence, right flank sequence and a repeat regionof bases positioned between a rightmost base of the left flank sequenceand a leftmost base of the right flank sequence, wherein the repeatregion includes a number of repeats of a repeated sequence of basescorresponding to a particular marker region of the plurality of markerregions; (2) for each of the sequence reads, aligning at least a portionthe left flank sequence with a reference left flank, wherein thereference left flank borders a reference repeat region of a referencenucleic acid sequence corresponding to the particular marker region; (3)for the repeat region corresponding to a target homopolymer in thesequence reads, calculating a histogram of homopolymer signal valuesbased on flow space signal measurements for the target homopolymer,wherein at least a portion of the marker regions corresponds to targethomopolymers; (4) determining a score per marker based on features ofthe histogram of homopolymer signal values for each marker regioncorresponding to the target homopolymers to produce a plurality ofscores; and (5) combining the plurality of scores to form a total MSIscore for the sample. The method may further comprise calculating ahistogram of repeat lengths for sequence reads corresponding to themarker region of the target STR, wherein a second portion of the markerregions corresponds to marker regions of target short tandem repeats(STR). The method may further comprise determining a score per STRmarker based on features of the histogram of repeat lengths to produce asecond plurality of scores. The step of determining a score per STRmarker may further comprise calculating a ratio of a second highestnumber of sequence reads to a first highest number of sequence reads inthe histogram of repeat lengths. The method may further compriseapplying a sigmoid function to the ratio. The step of combining theplurality of scores may further comprise combining the second pluralityof scores with the plurality of scores to form the total MSI score. Thestep of combining the plurality of scores may further comprisenormalizing the total MSI score based on a number of markers meeting aminimum coverage criterion. The method may obtain the total MSI scoreusing a tumor-only analysis. The method may obtain the total MSI scoreis obtained using a tumor-normal analysis. The step of calculating ahistogram of homopolymer signal values may further comprise calculatinga sum of M flow space signal measurements corresponding to M nucleotideflows of a sequence of flows having a same nucleotide type as the targethomopolymer to form the homopolymer signal value for the sequence read.For sequence reads including sequence reads in a forward direction andsequence reads in a reverse direction, the step of calculating ahistogram of homopolymer signal values may further comprise calculatinga first histogram of homopolymer signal values for the sequence reads inthe forward direction and a second histogram of homopolymer signalvalues for the sequence reads in the reverse direction. The features maybe based on a mean and a standard deviation of the homopolymer signalvalues. The step of determining a score per marker may further compriseapplying a sigmoid function to each of the features. The step ofdetermining a score per marker may further comprise calculating aweighted sum of the features. The step of combining the plurality ofscores may further comprise applying a threshold score to the score permarker. The step of combining the plurality of scores may furthercomprise determining whether the sequence reads associated with themarker region have a coverage level above a minimum coverage level. Thestep of combining the plurality of scores may further comprise summingthe scores of the plurality of scores that meet a threshold criterionand a coverage criterion to form the total MSI score.

According to an exemplary embodiment, there is providedcomputer-readable media comprising machine-readable instructions that,when loaded in a machine-readable memory and executed by the processor,are configured to cause a system to perform a method detectingmicrosatellite instability (MSI) in a sample, the method including: (1)receiving a plurality of nucleic acid sequence reads corresponding to aplurality of marker regions for MSI, wherein each of the sequence readsincludes a left flank sequence, right flank sequence and a repeat regionof bases positioned between a rightmost base of the left flank sequenceand a leftmost base of the right flank sequence, wherein the repeatregion includes a number of repeats of a repeated sequence of basescorresponding to a particular marker region of the plurality of markerregions; (2) for each of the sequence reads, aligning at least a portionthe left flank sequence with a reference left flank, wherein thereference left flank borders a reference repeat region of a referencenucleic acid sequence corresponding to the particular marker region; (3)for the repeat region corresponding to a target homopolymer in thesequence reads, calculating a histogram of homopolymer signal valuesbased on flow space signal measurements for the target homopolymer,wherein at least a portion of the marker regions corresponds to targethomopolymers; (4) determining a score per marker based on features ofthe histogram of homopolymer signal values for each marker regioncorresponding to the target homopolymers to produce a plurality ofscores; and (5) combining the plurality of scores to form a total MSIscore for the sample. The method may further comprise calculating ahistogram of repeat lengths for sequence reads corresponding to themarker region of the target STR, wherein a second portion of the markerregions corresponds to marker regions of target short tandem repeats(STR). The method may further comprise determining a score per STRmarker based on features of the histogram of repeat lengths to produce asecond plurality of scores. The step of determining a score per STRmarker may further comprise calculating a ratio of a second highestnumber of sequence reads to a first highest number of sequence reads inthe histogram of repeat lengths. The method may further compriseapplying a sigmoid function to the ratio. The step of combining theplurality of scores may further comprise combining the second pluralityof scores with the plurality of scores to form the total MSI score. Thestep of combining the plurality of scores may further comprisenormalizing the total MSI score based on a number of markers meeting aminimum coverage criterion. The method may obtain the total MSI scoreusing a tumor-only analysis. The method may obtain the total MSI scoreis obtained using a tumor-normal analysis. The step of calculating ahistogram of homopolymer signal values may further comprise calculatinga sum of M flow space signal measurements corresponding to M nucleotideflows of a sequence of flows having a same nucleotide type as the targethomopolymer to form the homopolymer signal value for the sequence read.For sequence reads including sequence reads in a forward direction andsequence reads in a reverse direction, the step of calculating ahistogram of homopolymer signal values may further comprise calculatinga first histogram of homopolymer signal values for the sequence reads inthe forward direction and a second histogram of homopolymer signalvalues for the sequence reads in the reverse direction. The features maybe based on a mean and a standard deviation of the homopolymer signalvalues. The step of determining a score per marker may further compriseapplying a sigmoid function to each of the features. The step ofdetermining a score per marker may further comprise calculating aweighted sum of the features. The step of combining the plurality ofscores may further comprise applying a threshold score to the score permarker. The step of combining the plurality of scores may furthercomprise determining whether the sequence reads associated with themarker region have a coverage level above a minimum coverage level. Thestep of combining the plurality of scores may further comprise summingthe scores of the plurality of scores that meet a threshold criterionand a coverage criterion to form the total MSI score.

According to an exemplary embodiment, there is provided a system fordetecting microsatellite instability (MSI), including a machine-readablememory and a processor configured to execute machine-readableinstructions, which, when executed by the processor, cause the system toperform a method for detecting MSI in a sample, the method including: 1)receiving a plurality of nucleic acid sequence reads corresponding to aplurality of marker regions for MSI, wherein each of the sequence readsincludes a left flank sequence, right flank sequence and a repeat regionof bases positioned between a rightmost base of the left flank sequenceand a leftmost base of the right flank sequence, wherein the repeatregion includes a number of repeats of a repeated sequence of basescorresponding to a particular marker region of the plurality of markerregions; (2) for each of the sequence reads, aligning at least a portionthe left flank sequence with a reference left flank, wherein thereference left flank borders a reference repeat region of a referencenucleic acid sequence corresponding to the particular marker region; (3)for the repeat region corresponding to a target homopolymer in thesequence reads, calculating a histogram of homopolymer signal valuesbased on flow space signal measurements for the target homopolymer,wherein at least a portion of the marker regions corresponds to targethomopolymers; (4) determining a score per marker based on features ofthe histogram of homopolymer signal values for each marker regioncorresponding to the target homopolymers to produce a plurality ofscores; and (5) combining the plurality of scores to form a total MSIscore for the sample. The method may further comprise calculating ahistogram of repeat lengths for sequence reads corresponding to themarker region of the target STR, wherein a second portion of the markerregions corresponds to marker regions of target short tandem repeats(STR). The method may further comprise determining a score per STRmarker based on features of the histogram of repeat lengths to produce asecond plurality of scores. The step of determining a score per STRmarker may further comprise calculating a ratio of a second highestnumber of sequence reads to a first highest number of sequence reads inthe histogram of repeat lengths. The method may further compriseapplying a sigmoid function to the ratio. The step of combining theplurality of scores may further comprise combining the second pluralityof scores with the plurality of scores to form the total MSI score. Thestep of combining the plurality of scores may further comprisenormalizing the total MSI score based on a number of markers meeting aminimum coverage criterion. The method may obtain the total MSI scoreusing a tumor-only analysis. The method may obtain the total MSI scoreis obtained using a tumor-normal analysis. The step of calculating ahistogram of homopolymer signal values may further comprise calculatinga sum of M flow space signal measurements corresponding to M nucleotideflows of a sequence of flows having a same nucleotide type as the targethomopolymer to form the homopolymer signal value for the sequence read.For sequence reads including sequence reads in a forward direction andsequence reads in a reverse direction, the step of calculating ahistogram of homopolymer signal values may further comprise calculatinga first histogram of homopolymer signal values for the sequence reads inthe forward direction and a second histogram of homopolymer signalvalues for the sequence reads in the reverse direction. The features maybe based on a mean and a standard deviation of the homopolymer signalvalues. The step of determining a score per marker may further compriseapplying a sigmoid function to each of the features. The step ofdetermining a score per marker may further comprise calculating aweighted sum of the features. The step of combining the plurality ofscores may further comprise applying a threshold score to the score permarker. The step of combining the plurality of scores may furthercomprise determining whether the sequence reads associated with themarker region have a coverage level above a minimum coverage level. Thestep of combining the plurality of scores may further comprise summingthe scores of the plurality of scores that meet a threshold criterionand a coverage criterion to form the total MSI score.

FIG. 12 shows an exemplary representation of flow space signalmeasurements from which base calls may be made. In this example, thex-axis shows the flow number and nucleotide that was flowed in a flowsequence. The bars in the graph show the amplitudes of the flow spacesignal measurements for each flow from a particular location of amicrowell in the sensor array. The numerals on the y-axis show thecorresponding number of nucleotide incorporations that may be estimatedby rounding to the nearest integer, for example. The number ofnucleotide incorporations indicates a homopolymer length. The flow spacesignal measurements may be raw acquisition data or data having beenprocessed, such as, e.g., by scaling, background filtering,normalization, correction for signal decay, and/or correction for phaseerrors or effects, etc. The base calls may be made by analyzing anysuitable signal characteristics (e.g., signal amplitude or intensity).The structure and/or design of sensor array, signal processing and basecalling for use with the present teachings may include one or morefeatures described in U.S. Pat. Appl. Publ. No. 2013/0090860, publishedApr. 11, 2013, incorporated by reference herein in its entirety.

For example, the nucleotide flow order is:

ACTGACTGAand the respective signals generated by a well after each nucleotideflow are:

-   -   0.1, 0.3, 0.2, 1.4, 0.3, 1.2, 0.8, 1.5, 0.7        Based on the nucleotide flow sequence, a putative nucleic acid        sequence is generated using the signals rounded to the nearest        integer (as either a nucleotide incorporation event occurred or        did not occur, but not partially). Thus, the above nucleotide        flow order and signals establish a putative nucleic acid        sequence as follows:

FLOW SIGNAL SEQUENCE MEASUREMENT BASE SEQUENCE A 0.1 C 0.3 T 0.2 G 1.4 →G A 0.3 C 1.2 → C T 0.8 → T G 1.5 → G A 0.7 → A

Once the base sequence for the sequence read is determined, the sequenceread may be aligned to a reference sequence to form aligned sequencereads. Methods for forming aligned sequence reads for use with thepresent teachings may include one or more features described in U.S.Pat. Appl. Publ. No. 2012/0197623, published Aug. 2, 2012, incorporatedby reference herein in its entirety. The aligned sequence reads areprovided to the processor, for example, in an aligned BAM file.

The BAM file format structure is described in “Sequence Alignment/MapFormat Specification,” Sep. 12, 2014(https://github.com/samtools/hts-specs). As described herein, a “BAMfile” refers to a file compatible with the BAM format. As describedherein, an unaligned BAM file refers to a BAM file that does not containaligned sequence read information and mapping quality parameters and analigned BAM file refers to a BAM file that contains aligned sequenceread information and mapping quality parameters.

Nucleic acid sequence data can be generated using various techniques,platforms or technologies, including, but not limited to: capillaryelectrophoresis, microarrays, ligation-based systems, polymerase-basedsystems, hybridization-based systems, direct or indirect nucleotideidentification systems, pyrosequencing, ion- or pH-based detectionsystems, electronic signature-based systems, etc.

Various embodiments of nucleic acid sequencing platforms, such as anucleic acid sequencer, can include components as displayed in the blockdiagram of FIG. 13 . According to various embodiments, sequencinginstrument 200 can include a fluidic delivery and control unit 202, asample processing unit 204, a signal detection unit 206, and a dataacquisition, analysis and control unit 208. Various embodiments ofinstrumentation, reagents, libraries and methods used for nextgeneration sequencing are described in U.S. Patent ApplicationPublication No. 2009/0127589 and No. 2009/0026082. Various embodimentsof instrument 200 can provide for automated sequencing that can be usedto gather sequence information from a plurality of sequences inparallel, such as substantially simultaneously.

In various embodiments, the fluidics delivery and control unit 202 caninclude reagent delivery system. The reagent delivery system can includea reagent reservoir for the storage of various reagents. The reagentscan include RNA-based primers, forward/reverse DNA primers,oligonucleotide mixtures for ligation sequencing, nucleotide mixturesfor sequencing-by-synthesis, optional ECC oligonucleotide mixtures,buffers, wash reagents, blocking reagent, stripping reagents, and thelike. Additionally, the reagent delivery system can include a pipettingsystem or a continuous flow system which connects the sample processingunit with the reagent reservoir.

In various embodiments, the sample processing unit 204 can include asample chamber, such as flow cell, a substrate, a micro-array, amulti-well tray, or the like. The sample processing unit 204 can includemultiple lanes, multiple channels, multiple wells, or other means ofprocessing multiple sample sets substantially simultaneously.Additionally, the sample processing unit can include multiple samplechambers to enable processing of multiple runs simultaneously. Inparticular embodiments, the system can perform signal detection on onesample chamber while substantially simultaneously processing anothersample chamber. Additionally, the sample processing unit can include anautomation system for moving or manipulating the sample chamber.

In various embodiments, the signal detection unit 206 can include animaging or detection sensor. For example, the imaging or detectionsensor can include a CCD, a CMOS, an ion or chemical sensor, such as anion sensitive layer overlying a CMOS or FET, a current or voltagedetector, or the like. The signal detection unit 206 can include anexcitation system to cause a probe, such as a fluorescent dye, to emit asignal. The excitation system can include an illumination source, suchas arc lamp, a laser, a light emitting diode (LED), or the like. Inparticular embodiments, the signal detection unit 206 can include opticsfor the transmission of light from an illumination source to the sampleor from the sample to the imaging or detection sensor. Alternatively,the signal detection unit 206 may provide for electronic or non-photonbased methods for detection and consequently not include an illuminationsource. In various embodiments, electronic-based signal detection mayoccur when a detectable signal or species is produced during asequencing reaction. For example, a signal can be produced by theinteraction of a released byproduct or moiety, such as a released ion,such as a hydrogen ion, interacting with an ion or chemical sensitivelayer. In other embodiments a detectable signal may arise as a result ofan enzymatic cascade such as used in pyrosequencing (see, for example,U.S. Patent Application Publication No. 2009/0325145) wherepyrophosphate is generated through base incorporation by a polymerasewhich further reacts with ATP sulfurylase to generate ATP in thepresence of adenosine 5′ phosphosulfate wherein the ATP generated may beconsumed in a luciferase mediated reaction to generate achemiluminescent signal. In another example, changes in an electricalcurrent can be detected as a nucleic acid passes through a nanoporewithout the need for an illumination source.

In various embodiments, a data acquisition analysis and control unit 208can monitor various system parameters. The system parameters can includetemperature of various portions of instrument 200, such as sampleprocessing unit or reagent reservoirs, volumes of various reagents, thestatus of various system subcomponents, such as a manipulator, a steppermotor, a pump, or the like, or any combination thereof.

It will be appreciated by one skilled in the art that variousembodiments of instrument 200 can be used to practice variety ofsequencing methods including ligation-based methods, sequencing bysynthesis, single molecule methods, nanopore sequencing, and othersequencing techniques.

In various embodiments, the sequencing instrument 200 can determine thesequence of a nucleic acid, such as a polynucleotide or anoligonucleotide. The nucleic acid can include DNA or RNA, and can besingle stranded, such as ssDNA and RNA, or double stranded, such asdsDNA or a RNA/cDNA pair. In various embodiments, the nucleic acid caninclude or be derived from a fragment library, a mate pair library, aChIP fragment, or the like. In particular embodiments, the sequencinginstrument 200 can obtain the sequence information from a single nucleicacid molecule or from a group of substantially identical nucleic acidmolecules.

In various embodiments, sequencing instrument 200 can output nucleicacid sequencing read data in a variety of different output data filetypes/formats, including, but not limited to: *.fasta, *.csfasta,*seq.txt, *qseq.txt, *.fastq, *.sff, *prb.txt, *.sms, *srs and/or *.qv.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented using appropriatelyconfigured and/or programmed hardware and/or software elements.Determining whether an embodiment is implemented using hardware and/orsoftware elements may be based on any number of factors, such as desiredcomputational rate, power levels, heat tolerances, processing cyclebudget, input data rates, output data rates, memory resources, data busspeeds, etc., and other design or performance constraints.

Examples of hardware elements may include processors, microprocessors,input(s) and/or output(s) (I/O) device(s) (or peripherals) that arecommunicatively coupled via a local interface circuit, circuit elements(e.g., transistors, resistors, capacitors, inductors, and so forth),integrated circuits, application specific integrated circuits (ASIC),programmable logic devices (PLD), digital signal processors (DSP), fieldprogrammable gate array (FPGA), logic gates, registers, semiconductordevice, chips, microchips, chip sets, and so forth. The local interfacemay include, for example, one or more buses or other wired or wirelessconnections, controllers, buffers (caches), drivers, repeaters andreceivers, etc., to allow appropriate communications between hardwarecomponents. A processor is a hardware device for executing software,particularly software stored in memory. The processor can be any custommade or commercially available processor, a central processing unit(CPU), an auxiliary processor among several processors associated withthe computer, a semiconductor based microprocessor (e.g., in the form ofa microchip or chip set), a macroprocessor, or generally any device forexecuting software instructions. A processor can also represent adistributed processing architecture. The I/O devices can include inputdevices, for example, a keyboard, a mouse, a scanner, a microphone, atouch screen, an interface for various medical devices and/or laboratoryinstruments, a bar code reader, a stylus, a laser reader, aradio-frequency device reader, etc. Furthermore, the I/O devices alsocan include output devices, for example, a printer, a bar code printer,a display, etc. Finally, the I/O devices further can include devicesthat communicate as both inputs and outputs, for example, amodulator/demodulator (modem; for accessing another device, system, ornetwork), a radio frequency (RF) or other transceiver, a telephonicinterface, a bridge, a router, etc.

Examples of software may include software components, programs,applications, computer programs, application programs, system programs,machine programs, operating system software, middleware, firmware,software modules, routines, subroutines, functions, methods, procedures,software interfaces, application program interfaces (API), instructionsets, computing code, computer code, code segments, computer codesegments, words, values, symbols, or any combination thereof. A softwarein memory may include one or more separate programs, which may includeordered listings of executable instructions for implementing logicalfunctions. The software in memory may include a system for identifyingdata streams in accordance with the present teachings and any suitablecustom made or commercially available operating system (0/S), which maycontrol the execution of other computer programs such as the system, andprovides scheduling, input-output control, file and data management,memory management, communication control, etc.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented using appropriatelyconfigured and/or programmed non-transitory machine-readable medium orarticle that may store an instruction or a set of instructions that, ifexecuted by a machine, may cause the machine to perform a method and/oroperations in accordance with the exemplary embodiments. Such a machinemay include, for example, any suitable processing platform, computingplatform, computing device, processing device, computing system,processing system, computer, processor, scientific or laboratoryinstrument, etc., and may be implemented using any suitable combinationof hardware and/or software. The machine-readable medium or article mayinclude, for example, any suitable type of memory unit, memory device,memory article, memory medium, storage device, storage article, storagemedium and/or storage unit, for example, memory, removable ornon-removable media, erasable or non-erasable media, writeable orre-writeable media, digital or analog media, hard disk, floppy disk,read-only memory compact disc (CD-ROM), recordable compact disc (CD-R),rewriteable compact disc (CD-RW), optical disk, magnetic media,magneto-optical media, removable memory cards or disks, various types ofDigital Versatile Disc (DVD), a tape, a cassette, etc., including anymedium suitable for use in a computer. Memory can include any one or acombination of volatile memory elements (e.g., random access memory(RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements(e.g., ROM, EPROM, EEROM, Flash memory, hard drive, tape, CDROM, etc.).Moreover, memory can incorporate electronic, magnetic, optical, and/orother types of storage media. Memory can have a distributed architecturewhere various components are situated remote from one another, but arestill accessed by the processor. The instructions may include anysuitable type of code, such as source code, compiled code, interpretedcode, executable code, static code, dynamic code, encrypted code, etc.,implemented using any suitable high-level, low-level, object-oriented,visual, compiled and/or interpreted programming language.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented at least partly using adistributed, clustered, remote, or cloud computing resource.

According to various exemplary embodiments, one or more features of anyone or more of the above-discussed teachings and/or exemplaryembodiments may be performed or implemented using a source program,executable program (object code), script, or any other entity comprisinga set of instructions to be performed. When a source program, theprogram can be translated via a compiler, assembler, interpreter, etc.,which may or may not be included within the memory, so as to operateproperly in connection with the O/S. The instructions may be writtenusing (a) an object oriented programming language, which has classes ofdata and methods, or (b) a procedural programming language, which hasroutines, subroutines, and/or functions, which may include, for example,C, C++, R, Pascal, Basic, Fortran, Cobol, Perl, Java, and Ada.

According to various exemplary embodiments, one or more of theabove-discussed exemplary embodiments may include transmitting,displaying, storing, printing or outputting to a user interface device,a computer readable storage medium, a local computer system or a remotecomputer system, information related to any information, signal, data,and/or intermediate or final results that may have been generated,accessed, or used by such exemplary embodiments. Such transmitted,displayed, stored, printed or outputted information can take the form ofsearchable and/or filterable lists of runs and reports, pictures,tables, charts, graphs, spreadsheets, correlations, sequences, andcombinations thereof, for example.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

What is claimed is:
 1. A system for detecting microsatellite instability(MSI), including: a machine-readable memory; and a processor configuredto execute machine-readable instructions, which, when executed by theprocessor, cause the system to perform a method for detecting MSI in asample, comprising: receiving a plurality of nucleic acid sequence readscorresponding to a plurality of marker regions for MSI, wherein each ofthe sequence reads includes a left flank sequence, right flank sequenceand a repeat region of bases positioned between a rightmost base of theleft flank sequence and a leftmost base of the right flank sequence,wherein the repeat region includes a number of repeats of a repeatedsequence of bases corresponding to a particular marker region of theplurality of marker regions; for each of the sequence reads, aligning atleast a portion the left flank sequence with a reference left flank,wherein the reference left flank borders a reference repeat region of areference nucleic acid sequence corresponding to the particular markerregion; for the repeat region corresponding to a target homopolymer inthe sequence reads, calculating a histogram of homopolymer signal valuesbased on flow space signal measurements for the target homopolymer,wherein at least a portion of the marker regions corresponds to targethomopolymers; determining a score per marker by calculating a weightedsum of features of the histogram of homopolymer signal values for eachmarker region corresponding to the target homopolymers to produce aplurality of scores; and combining the plurality of scores to form atotal MSI score for the sample.
 2. The system of claim 1, wherein asecond portion of the marker regions corresponds to target short tandemrepeats (STRs), the method further comprising calculating a histogram ofrepeat lengths for sequence reads corresponding to the marker region ofthe target STR.
 3. The system of claim 2, wherein the method furthercomprises determining a score per STR marker based on features of thehistogram of repeat lengths to produce a second plurality of scores. 4.The system of claim 2, wherein the step of determining a score per STRmarker further comprises calculating a ratio of a second highest numberof sequence reads to a first highest number of sequence reads in thehistogram of repeat lengths.
 5. The system of claim 4, wherein themethod further comprises applying a sigmoid function to the ratio. 6.The system of claim 3, wherein the step of combining the plurality ofscores further comprises combining the second plurality of scores withthe plurality of scores to form the total MSI score.
 7. The system ofclaim 1, wherein the step of combining the plurality of scores furthercomprises normalizing the total MSI score based on a number of markersmeeting a minimum coverage criterion.
 8. The system of claim 1, whereinthe total MSI score is obtained using a tumor-only analysis.
 9. Thesystem of claim 1, wherein the total MSI score is obtained using atumor-normal analysis.
 10. The system of claim 1, wherein the step ofcalculating a histogram of homopolymer signal values further comprisescalculating a sum of M flow space signal measurements corresponding to Mnucleotide flows of a sequence of flows having a same nucleotide type asthe target homopolymer to form the homopolymer signal value for thesequence read.
 11. The system of claim 1, wherein the sequence readsinclude sequence reads in a forward direction and sequence reads in areverse direction, wherein the step of calculating a histogram ofhomopolymer signal values further comprises calculating a firsthistogram of homopolymer signal values for the sequence reads in theforward direction and a second histogram of homopolymer signal valuesfor the sequence reads in the reverse direction.
 12. The system of claim1, wherein the features are based on a mean and a standard deviation ofthe homopolymer signal values.
 13. The system of claim 1, wherein thestep of determining a score per marker further comprises applying asigmoid function to each of the features.
 14. The system of claim 1,wherein the step of combining the plurality of scores further comprisesapplying a threshold score to the score per marker.
 15. The system ofclaim 1, wherein the step of combining the plurality of scores furthercomprises determining whether the sequence reads associated with themarker region have a coverage level above a minimum coverage level. 16.The system of claim 1, wherein the step of combining the plurality ofscores further comprises summing the scores of the plurality of scoresthat meet a threshold criterion and a coverage criterion to form thetotal MSI score.
 17. A computer-readable media comprisingmachine-readable instructions that, when loaded in a machine-readablememory and executed by a processor, are configured to cause a system toperform a method for detecting microsatellite instability (MSI) in asample, said method comprising: receiving a plurality of nucleic acidsequence reads corresponding to a plurality of marker regions for MSI,wherein each of the sequence reads includes a left flank sequence, rightflank sequence and a repeat region of bases positioned between arightmost base of the left flank sequence and a leftmost base of theright flank sequence, wherein the repeat region includes a number ofrepeats of a repeated sequence of bases corresponding to a particularmarker region of the plurality of marker regions; for each of thesequence reads, aligning at least a portion the left flank sequence witha reference left flank, wherein the reference left flank borders areference repeat region of a reference nucleic acid sequencecorresponding to the particular marker region; for the repeat regioncorresponding to a target homopolymer in the sequence reads, calculatinga histogram of homopolymer signal values based on flow space signalmeasurements for the target homopolymer, wherein at least a portion ofthe marker regions corresponds to target homopolymers; determining ascore per marker by calculating a weighted sum of features of thehistogram of homopolymer signal values for each marker regioncorresponding to the target homopolymers to produce a plurality ofscores; and combining the plurality of scores to form a total MSI scorefor the sample.
 18. The computer-readable media of claim 17, wherein asecond portion of the marker regions corresponds to target short tandemrepeats (STRs), the method further comprising calculating a histogram ofrepeat lengths for sequence reads corresponding to the marker region ofthe target STR.
 19. The computer-readable media of claim 17, wherein thefeatures are based on a mean and a standard deviation of the homopolymersignal values.
 20. The computer-readable media of claim 17, wherein thestep of determining a score per marker further comprises applying asigmoid function to each of the features.